Adharsh Murali
University of Michigan
6 Papers
3 Citations
Adharsh Murali is an academic researcher from University of Michigan. The author has contributed to research in topics: Medicine & Cohort. The author has an hindex of 1, co-authored 3 publications.
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Papers
Hybrid bag of approaches to characterize selection criteria for cohort identification
V. G. Vinod Vydiswaran,Asher Strayhorn,Xinyan Zhao,Phil Robinson,Mahesh Agarwal,Erin Bagazinski,Madia Essiet,Bradley E. Iott,Hyeon Joo,PingJui Ko,Dahee Lee,Jin Xiu Lu,Jinghui Liu,Adharsh Murali,Koki Sasagawa,Tianshi Wang,Nalingna Yuan +16 more
TL;DR: There is merit in continuing a focused criterion-specific analysis and developing appropriate knowledge resources to build a quality cohort selection system to address the class imbalance in the cohort selection identification task.
24
Predicting persistent opioid use after surgery using electronic health record and patient-reported data.
Karandeep Singh,Adharsh Murali,Haley Stevens,V. G. Vinod Vydiswaran,Amy S.B. Bohnert,Chad M. Brummett,Anne C. Fernandez +6 more
TL;DR: In this paper , the authors developed and validated a model to predict persistent opioid use after surgery, defined as filling opioid prescriptions in post-discharge days 4 to 90 and 91 to 180.
4
Mp33-17 using a clinical registry and machine learning to predict ureteral stent placement following ureteroscopy
Michael Inadomi,Khurshid R. Ghani,Tae Kim,Casey A. Dauw,John M. Hollingsworth,David Leavitt,Adharsh Murali,Kavya Swarna,Karandeep Singh +8 more
TL;DR: A model predicting ureteral stent placement following URS can inform patient decision-making using a set of eight predictors and was generally well-calibrated and thus suitable for informing patients.
3
Simplified cardiovascular index may be the best comorbidity index for clinical use in prediction of mortality for renal cancer patients.
Dennis N. Boynton,Sabrina L. Noyes,Adharsh Murali,Henry Peabody,Andrew Krumm,Karandeep Singh,B. Lane +6 more
- 18 Jan 2024
TL;DR: Increasing comorbidity, age, tumor size, and cM stage are predictors of ACM for suspected renal cancer patients, and CVI appears to provide comparable information to various iterations of CCI (uCCI, aCCI, aCCI while being the simplest to use.
1
Re-ranking Biomedical Literature for Precision Medicine with Pre-trained Neural Models
Jiazhao Li,Adharsh Murali,Qiaozhu Mei,V. G. Vinod Vydiswaran +3 more
- 01 Nov 2020
TL;DR: In this article, a biomedical literature retrieval approach that incorporates a domain-specific BERT model as an auxiliary re-ranker was proposed, which improved retrieval performance by 6.2% in inferred NDCG and 6.8% in R-precision.